Using Typestyles to Prioritize and Rank Search Results

ABSTRACT

Computer-based search results are improved by taking in consideration emphasized content by extracting content of a data corpus items indicated by typestyle emphasis; indexing the extracted emphasized content in the searched corpus; in response to a natural language query from a requester, performing a search such as a deep question and answer search of the corpus including the indexed emphasized content; and producing search results to the requester from the corpus with preference in the order or presentation of the results according to the emphasized content.

CROSS-REFERENCE TO RELATED APPLICATIONS (CLAIMING BENEFIT UNDER 35U.S.C. 120)

This application is a continuation of U.S. patent application Ser. No.14/151,462, our docket AUS920130292US1, filed on Jan. 9, 2014, by PaulR. Bastide, et al.

FEDERALLY SPONSORED RESEARCH AND DEVELOPMENT STATEMENT

None.

MICROFICHE APPENDIX

Not applicable.

INCORPORATION BY REFERENCE

None.

FIELD OF THE INVENTION

This application is a continuation of U.S. patent application Ser. No.14/151,462, our docket AUS920130292US1, filed on Jan. 9, 2014, by PaulR. Bastide, et al. This invention relates generally to methods, systems,computer program products and automated processes for using typestyleand extended font characteristics to rank and prioritize results of aquestion-and-answer search using Natural Language Processing.

BACKGROUND OF INVENTION

In document publishing, typestyles including, but not limited to,different font sizes, font colors, highlighting, italicization,underlining, and strikethrough indicate different meanings and tones forthe reader's interpretation that extend beyond the text words andsentences themselves. Different cultures have different ways to markemphasis in desktop publishing, for example.

Consider the three statements, which are composed of the same words andsentence structures, but two of which include additional informationconveyed by italicization:

-   -   We were eating apples. (plain statement, no further information)    -   We were eating apples. ( . . . and not some other fruit)    -   We were eating apples. ( . . . but not now)

Most cultures have similar ways of conveying additional emphasis, and insome cases, the context of the text may also play a part in what methodsof emphasis are available. For example, in Short Message Service (SMS)or “text messaging” services, there is no typestyle functionality, justplain text. So, the foregoing example may appear with ad hoc emphasis asfollows:

-   -   We were eating apples. (plain statement, no further information)    -   We were eating APPLES. ( . . . and not some other fruit)    -   We *were* eating apples. ( . . . but not now)

Here, society has made use of all capitalization (e.g. all upper casefont) and bracketing with asterisks to convey some additionalinformation beyond the simple, plain text message.

SUMMARY OF THE INVENTION

Computer-based search results are improved by taking in considerationemphasized content by extracting content of a data corpus itemsindicated by typestyle emphasis; indexing the extracted emphasizedcontent in the searched corpus; in response to a natural language queryfrom a requester, performing a search such as a deep question and answersearch of the corpus including the indexed emphasized content; andproducing search results to the requester from the corpus withpreference in the order or presentation of the results according to theemphasized content.

BRIEF DESCRIPTION OF THE DRAWINGS

The figures presented herein, when considered in light of thisdescription, form a complete disclosure of one or more embodiments ofthe invention, wherein like reference numbers in the figures representsimilar or same elements or steps.

FIGS. 1a and 1b depicts two embodiment options of the present inventionfor integration to or cooperation with a search engine system.

FIG. 2 provides more details of a logical process according to thepresent invention.

FIG. 3 illustrates three examples of operation, and varying resultsbased on different languages and different emphasized content.

FIG. 4 sets forth a generalized computing platform suitable forcombination with program instructions to perform a logical process toyield a computer system embodiment according to the present invention.

DETAILED DESCRIPTION OF EMBODIMENT(S) OF THE INVENTION

The present inventors have recognized a problem in the art that appearsto be unrecognized and unsolved by those skilled in the art relating toinput and output of common search engines as well as for advanced searchengines such as deep Question and Answer (deep QA) search engines. Theforegoing emphasis in text as conveyed by typestyles, whether it is partof a query string input into a search engine or it is part of thetextual findings (the search results), is ignored. In fact, thetechnology of the existing art teaches away from considering thisinformation by ignoring and stripping from text these typestyleindicators.

For example, if one is to go to a Google™ search page, and type a searchstring into the search box, one will find no functionality to add bold,italics, or underlining to the input string. If one tries to be cleverand create such a string with typestyle-based emphasis in anotherprogram, such as Microsoft Word™, and then to cut-and-paste that stringwith emphasis into the Google™ search input box, one will bedisappointed when the text is placed in the box without any specialemphasis formatting.

Likewise, the results of search operations do not place any greaterweight or ranking priority on result items which contain emphasized textrelevant to the search query. Even though the publisher of a documentmay convey special emphasis within a page or document by using typestyleindicators, search engines do not use that information to rank thedocument or page higher or lower in the results.

The inventors have recognized this problem in the art, e.g. thatvaluable information conveyed in typestyle meta-data is being ignored,information which could be used to improve search results at the input,at the output, or both.

Because emphasis using typestyles is related to natural language, thepresent invention is set forth in terms of enhancements in andcooperation with a deep QA natural language processing system, but itshould be understood by those skilled in the art that such enhancementsmay be beneficial to other types of search engines as well.

Survey for an Existing Solution

Initially, the present inventors looked for an existing solution forthis problem. Having found no suitable solution, and found no suggestionthat anyone in the art has recognized this lost information in bothsearch engine queries and search engine results, the present inventorsset out to develop a new system, a method and computer program product,all of which are disclosed in the following paragraphs in particulardetail.

Deep Semantic Analysis of Natural Language Text in General

Deep QA search engines employ analysis which detects deep semanticrelationships between terms and phrases within a document, web page, orother textual sources, to beyond simple search term (key) finding andfrequency of occurrence ranking. The term “deep semantic relationships”,for the purposes of the present disclosure, is meant to refer torelationships between information entities in a given context and howthey relate to each other. They can be the occurrence of triple storeterms or entities or they can be the occurrence with a relationship ofthose entities. For example, (Mutation, Cancer, Organ) would be asemantic relationship, identifying that mutations, cancer and specificorgan ontologies have a deep relationship. Further, a deep semanticanalysis system sometimes associates a specific relationship (mass,?indicates, metastasis), where the combination and synonyms for“indicates” would mean the cancer has metastasized.

The term deep semantic relationship may also refer to the relationshipof terms in a specific ontology and their similarity when expressed inpassages of text based on the how they are typically expressed usingsequence matching algorithms for text analysis. For example, thewell-known Smith-Waterman sequence-matching algorithm measures thelengths of the longest similar subsequence between two texts, which isthen a measured or detected semantic relationship between those texts.

Deep semantic relationships consider the meaning of words within thecontext and structure of a sentence. They signify a “deep” understandingthe meaning of words that comprise a relationship within the sentence.Deep semantic relationships are usually developed with a very specificuse case in mind. For example, consider the sentence “John bought breadat the store.” From this, a relationship like sold(store, bread) may bemined, indicating that the store sold bread. This relationship requiresa deep understanding of what a store is (a retailer that sellsconsumable goods) and that bread is one of those items.

For example, one “specific use” in which deep semantic analysis has beenproposed is the deep semantic interpretations of legal texts as proposedby L. Thorne McCarty of Rutgers University (Association of ComputerMachinery (ACM), 971-1-59593-680). Another useful publicly-availabledocument regarding realization of a general purpose automatic deepsemantic analyzer of natural language text is described in “DeepSemantic Analysis of Text” by James F. Allen, et al., of the Universityof Rochester and the Institute for Human and Machine Cognition (documentW08-0227 from the ACL).

So, while deep semantic analysis of natural language text in general hasbeen discussed in the public domain, the inventors have discovered thatthe aforementioned problem of making an automated analysis of one ormore works of literature, those presently engaged in the art appear tobe focused on keyword searching and relevance ranking according tokeywords. One approach to advancing beyond keyword searching is“intent-centric” processing as proposed by Scott Brave, et al., in WIPOpatent application WO 2009/021198 A1. Inventors do not believe thisapproach, however solves the present problem because it addresses adifferent problem using a different approach without employing deepsemantic analysis.

The present invention is set forth in at least one exemplary embodimentas an application of or manner of using a deep semantic analyzerplatform. This platform may be a system such as the IBM Watson™ system,such as is described in “Building Watson: An Overview of the DeepQAProject” (Stanford University online, and AI Magazine, Fall 2010 issue).The foundation deep semantic analysis platform may be an alternategeneral-purpose deep semantic analyzer implementation such as thesemantic extraction component of the system described by Anna Stavrianouin United States Pre-Grant Published Patent Application 2013/0218914 A1(Aug. 22, 2013) suitably modified to include the functionality of therelated, incorporated patent application and that described herein bythe present inventors. Other useful, publicly-available teachingsregarding the availability of general purpose deep semantic analyzerswhich may be suitable for adapting and improving to the presentinvention may include those described by Konstantin Zuev in UnitedStates Pre-Grant Published Patent Application 2011/0270607 A1 (Nov. 3,2011); the Thompson's Motif-Index Literature system of Thiery Declerk,et al., as published in “Research and Advanced Technology for DigitalLibraries: Lecture Notes in Computer Science”, vol. 6966, 2011, pp.151-158; and using natural language parsers such as that described bySala Ait-Mokhtar, et al., in U.S. Pat. No. 7,058,567 (Jun. 6, 2006).

One may contrast deep semantic relationships with shallow semanticrelationships, that latter of which usually only consider the structureof parts of speech within a sentence, and not necessarily the meaningsof those words. An example shallow relationship may simply be of theform sentence(subject, verb, object). In the above example, this wouldbe sentence(john, bought, bread). These terms don't signify any specialmeaning, but their parts of speech form a shallow relationship called“sentence”.

Graphical logical forms for representation of text can be created usingone of several known methods, such as that proposed by James F. Allen,Mary Swift, and Will de Beaumont, of the University of Rochester and theInstitute for Human and Machine Cognition (Association for ComputerLinguistics (ACL), anthology document W08-2227).

Emphasis Indication Via Typestyle and Shifts in Typestyle

For the disclosure of the present invention and the several exemplaryembodiments, we will use the term “emphasized typestyle” to refer toelements of typography which effect the appearance of a word, characteror symbol, that imparts some culturally understood emphasis (or extendedmeaning) beyond that conveyed by a non-emphasized (“plain”) typestyle.In English, this may be bolding or italicization of a word in the samefont as the rest of a phrase or sentence, and it can mean the use of adifferent type face from the rest of the phrase or sentence in which itappears, such as:

-   -   This sentence is in Arial type face, and bolding shows emphasis.

Or:

-   -   This sentence is in Arial type face, and Times Roman font face        shows emphasis.

In other languages, other typestyle indicators may be employed to conveyemphasis. For example, to emphasis a part of a sentence in MandarinChinese, that part is preceded by shi (

), and followed by with de (

). In Modern Standard Arabic (MSA) script, capitalization (upper case)is rare, but capitalization can be used to show emphasis, as canunderlining a character or placing a dot (a diacritical mark) below thecharacter.

In some language encoding schemes, typestyle application isstraightforward to detect, such as in eXtendable Markup Language (XML).In XML, there is an opening tag and a closing tag which shifts therendering of a phrase, word or character from the default rendering, soa bolded word “apple” may be coded in XML as <bold> apple </bold>, forexample. Other character encoding schemes such as Unicode allow for morecomplex diacritical changes to the appearance of characters, such asstriking over one character with a second character, which can also beanalyzed to find any typestyle shifts which culturally indicateemphasis.

In many, but not all, situations, it is actually a shift in typestylefrom the “normal” typestyle of the rest of the text which showsemphasis. For example, if a paragraph is entirely written in non-boldtext, but only a few words of the paragraph are bolded, then the shiftfrom non-bold to bold (and back again) forms a bracketing around thebold text to emphasis it. If, however, the entire text was conveyed inbolded text, then bold typeface may not be an emphasis indicator, unlessthe paragraph is part of a larger document which conveys most its textin non-bolded typestyle. If the latter is true, then the entire boldedparagraph may be emphasized relative to the rest of the text of thedocument.

Similarly, changes in font face (e.g., Arial to Times Roman and back toArial), font size (e.g., increasing of size of text and then decreasingof size of text), and changes in color of text (e.g. changing from blacktext to red text and back to black text) are other well-known means oftypestyle shifts to indicate emphasis.

So, by “typestyle shift”, we will collectively refer to changes intypestyle from a normal or non-emphasized typestyle, typicallyreferenced to surrounding text. We may also use “typestyle shift” torefer to culturally-known typestyle indicators which convey emphasiswithout any surrounding “normal” text, such as the now-dropped HTML“blink” tag (replaced by cascading style sheet formatting).

“White space” may also be used to indicate emphasis, or de-emphasis(e.g. subordination). For example, in generalized English, an author mayuse indentation (e.g. increased white space between the left margin andthe beginning of a text line) to indicate subordinate clauses, examples,or species within a genus. One might pose a natural language question inwhich tabs or indentation create additional left-margin white space toshow examples (species) of a larger class (genus) or logical conditions,as such:

-   -   What are the ways to pay for college that:        -   do not include borrowing,        -   are available to young people with no prior work experience,            and        -   provide a reasonable rate of pay.

In the second element (“available to people with no experience”), ahanging indentation provides even more white space between the leftmargin and the beginning of the text to denote second and subsequentlines of text belong with the first line of text of the second element.Such use of white space, page layout, text placement, and alignment(tables, columns)—can also be used to provide emphasis or de-emphasis,depending on the context of the script and the cultural norms from whichthe text is drawn.

Embodiments of the present invention may detect any or all of theseemphasis designations which are encoded in digital representations oftext (e.g., ASCII, UniCode, XML, HTML, etc.), and which cause a humanreader to perceive a typestyle indication of emphasis when the digitalrepresentation is converted or processed to a human-readable outputdevice such as a printer or computer screen using a rendering languageor process (e.g. Printer Control Language (PCL), Hewlett-PackardGraphics Language (HPGL), PostScript, an API to a graphics processor,etc.).

OVERVIEW OF PRESENT INVENTION

Methods and systems according to the present invention enhance the useof typography information in search engines, especially in question andanswer systems, by:

-   -   1—Loading Data with the typography metadata of the text which        includes font family, font size, color coding, emphasis symbols        and etc. when ranking the search results.    -   2—Preprocessing each data element to assign a unique identifier        to lookup the corresponding typography metadata.    -   3—Generating a Model from the Loaded Data taking into account        the typography factors.

The advantages of the invention include improving the processing of datainto text mining systems, and enhancing search engines and especiallyQuestion and Answer Systems such as IBM's Watson™ and similar QAsystems.

Intended User Experience

Prior to discussing the exemplary embodiments of the invention, wedisclosed the intended user experience respective to a QA search on asystem such as IBM's Watson:

-   -   1. User logs into Q/A System.    -   2. Optionally, the user chooses the set of data (corpus) to be        loaded or searched.    -   3. The user inputs question in natural language format. Example        “what are the treatments for high cholesterol?”    -   4. The enhanced QA System analyzes the question using NLP.    -   5. The enhanced QA System searches the corpus and discovers a        set of search results.    -   6. The enhanced QA System assigns each data element in the        corpus (e.g. the data to be searched) a unique identifier to        lookup the corresponding typography metadata. From the search        results:        -   a. The enhanced QA System extracts font family, font size,            and font color information from each document from search            results.        -   b. The enhanced QA System extracts additional emphasis sign            information from each data item (documents, pages, database            records, etc.) from the search results, based on each data            item's origin and language.        -   c. The enhanced QA System extracts the location of the            document origin information from each document from search            results.        -   d. The enhanced QA System generates a Model from the Loaded            Data (corpus) taking into account the typography factors.            This model contains the data extracted from the steps above.    -   7. The enhanced QA System ranks the search results based on the        typography data and derives which data is the most relevant to        the question.    -   8. In the answer returned, the text can contain bold font and        plain font. Some of the texts are underlined. The system also        identifies the origin of the text was composed from a document        published within the United States. For the above example        Natural Language Query (NLQ), nine search results (R1-R9) may        appear with typestyles as follows:        -   R1=“Eating better”        -   R2=“Statin drug”        -   R3=“Bile acid resins like Colestid, Lo-Cholest, Prevalite,            Questran, and WelChol. They stick to cholesterol in the            intestines and prevent it from being absorbed. They can            lower LDL cholesterol by 15-30%.”        -   R4=“Ezetimibe (Zetia) blocks some of the cholesterol from            being absorbed by your body. It can lower LDL levels by            18-25%.”        -   R5=“Maintaining (or losing) weight is recommended as a first            step towards improving cholesterol levels . . . ”        -   R6=“Exercising more”        -   R7=“Fibric acid like Antara, Atromid, Lopid, and Tricor.            They reduce your triglycerides and may give a mild boost to            your HDL.”        -   R8=“Niacin, available as Niacor, Niaspan, and Nicolar.            Niacin modestly lowers LDL cholesterol and triglycerides and            can raise HDL cholesterol at low doses. LDL levels are            usually cut by 5-15%.”        -   R9=“A combination medicine like ezetimibe with simvastatin            (Vytorin) which uses a statin to block production of            cholesterol and ezetimibe to prevent cholesterol from being            absorbed.”    -   9. The enhanced QA System interprets that bold fonts and        underlined text are clear signs of emphasis in a text published        in the United States, and uses that information as an additional        factor to weigh and rank the search results.    -   10. The enhanced QA System normalizes font attributes to        prioritize the loading of document data.    -   11. The enhanced QA System presents a confidence level of each        of the data items in the search results. For this example, the        search results data items R1, R2, R5 and R6 (“Eating better”,        “Maintaining (or losing) weight, “Exercising more, Statin”) are        assigned with the highest rank in the search results due to the        emphasis in their typestyles. The results are re-prioritized,        re-ordered or re-ranked for presentation to the user as follows:        -   “Eating better”        -   “Statin drug”        -   “Maintaining (or losing) weight is recommended as a first            step towards improving cholesterol levels . . . ”        -   “Exercising more”        -   “Bile acid resins like Colestid, Lo-Cholest, Prevalite,            Questran, and WelChol. They stick to cholesterol in the            intestines and prevent it from being absorbed. They can            lower LDL cholesterol by 15-30%.”        -   “Ezetimibe (Zetia) blocks some of the cholesterol from being            absorbed by your body. It can lower LDL levels by 18-25%.”        -   “Fibric acid like Antara, Atrom id, Lopid, and Tricor. They            reduce your triglycerides and may give a mild boost to your            HDL.”        -   “Niacin, available as Niacor, Niaspan, and Nicolar. Niacin            modestly lowers LDL cholesterol and triglycerides and can            raise HDL cholesterol at low doses. LDL levels are usually            cut by 5-15%.”        -   “A combination medicine like ezetimibe with simvastatin            (Vytorin) which uses a statin to block production of            cholesterol and ezetimibe to prevent cholesterol from being            absorbed.”    -   12. In systems suitably equipped with learning functionality,        such as the IBM Watson system, the user reviews the presented        search results, and then indicates satisfaction or        dissatisfaction with the answer, such as by an overall        satisfaction level of all the items in the results or even by        individual indicators of satisfaction with each item within the        search results (e.g., find more like these results, and find        less like these results). Such information is then used to        “learn” from the user, which can be used to further enhance the        next search presentation of the same or a similar question to        the same user or to other users. In some learning search        engines, each user has a stored profile or history which records        his or her satisfaction ratings so that searches can be tailored        to his or her language and preferences. In yet some other        learning search systems, such learned satisfaction feedback can        be aggregated over multiple users and employed during searches        for other users, thereby achieving something akin to a        community-based preference profile.

First Exemplary Embodiment

Referring now to FIG. 1a , a first exemplary embodiment in which inputto a NLP search engine (QA search engine) output (search results) arere-prioritized according to typestyle-emphasized text found in theoutput, and optionally corresponding to typestyle-emphasized text in theoriginal query.

A user of a computing device (100) (or a client process) submits anatural language query (NLQ), which may contain (or may not)typestyle-emphasized words or phrases. The NLQ (101) is submitted to aNLP QA system (102), and optionally is received by the TypestyleProcessor (110) enhancement according to the present invention. The NLPQA system (102) searches its corpus of information (database records,documents, web pages, APIs to information systems, etc.), and returns(103) what is considered the most relevant results without considerationof typestyle-emphasized text in the search results (e.g. normal searchresults).

The Typestyle Processor (110) intercepts those results (103) prior todelivery to the requester (100), and analyzes them as previouslydiscussed using rule sets, synonym lists, thesauri, exception rules(111), and optionally one or more user preferences (104). These (111,104) allow the Typestyle Processor (110) to detect the native languagein which the results are expressed, and optionally the language of theNLQ. Based on the native language detection, one or more cultural rulesmay be retrieved which indicate how emphasis is made in that languageusing typestyles (e.g. bolding, italicization, capitalization,diacritical marks, framing with special characters, etc.). The TypestyleProcessor then evaluates the results (103) from the NLP search, andassigns confidence scores which are increased by finding emphasizedwords and phrases within the results (103) which are relevant to theNLQ. For additional accuracy, the NLQ may also be evaluated to findemphasized words and phrases, which are used to further increase theconfidence levels of each item in the search result which is relevant tothose emphasized words, phrases and terms.

The results (103) are then re-sorted, re-ranked, and re-prioritizedaccording to their initial relevance and further according to theconfidence levels created from the typestyle emphasis consideration.These re-sorted, re-ranked, or re-prioritized results (103′) are thendelivered to the requester (100), be it a client device or anothercomputing process.

It should be noted that the NLP QA system (102) may be the IBM Watson™system, but it may also be a suitable alternative QA system thatsupports natural language processing. It should also be noted that suchNLP searching may be invoked remotely, such as via a cloud-basedservice, and as such, FIG. 1a (and 1 b) do not imply direct or nativeintegration with such an NLP searching system.

Second Exemplary Embodiment

Referring now to FIG. 1b , an alternative embodiment according to thepresent invention is shown. In this embodiment, rather than modify theNLP search results according to typestyle emphasis evaluation, the queryitself (101) is modified according to typestyle emphasis analysis tocause the NLP search results to be prioritized and ranked with theemphasis in consideration. The modified NLQ (101′) is submitted to theNLP system (102) instead of the unmodified NLQ (101).

For example, consider a NLQ from a requester as follows with theitalicized emphasis:

-   -   “What are the best ways to discipline a child without spanking?”

Here, the language is Generalized English, which can be detected fromthe vocabulary and sentence structure. Child discipline is detected asthe object of the query, and because English speakers often use italicstypestyle to convey emphasis, it is determined that results avoidingspanking are to be raised in priority or rank, while results includingspanking are to be lowered in priority or ranking. So, rather thansubmitting the original (unmodified) NLQ to the NLP search system, theNLQ is modified to read as follows:

“What are the best ways to discipline a child with strong preference onmethods other than spanking.”

The added wording to this modified NLQ will cause the NLP system (102)to rank the results without spanking even higher than it would have withjust the phrase “without spanking”.

Detailed Logical Process Embodiment

In FIG. 2, a more detailed view of a logical process is shown of atleast one embodiment of the Typestyle Emphasis Processor (110), suitablefor realization as a combination of computer program product, programinstructions, computer processors, with or without specializedintegrated circuits to perform some or all of the logical operations.

This embodiment starts (200) by determining if the NLQ is to beintercepted (201). If so, then typestyle information is extracted (210)from the NLQ (101). A model (212) is built (211) representing theemphasis contained within the original NLQ, and then the NLQ is revised(213) per that model (212) to place additional natural languageconstraints into the NLQ which is submitted to the NLP search engine(102). If the NLQ is not to be revised, then the original NLQ (101) issubmitted to the NLP search engine (102).

After the NLP search engine results are intercepted (203), typestyleemphasis information is extracted (204) from the search results, andmodel (206) is built representing the emphasis found in each data item(e.g. documents, pages, database records, etc.) of the search results.Finally, the one or two models (212, 206) are compared to the searchresults, and confidence factors are assigned to each data item, whereinconfidence is enhanced by the matching emphasis found, and reduced (orleft neutral) for lack of emphasis. Then, the results of the search arere-prioritized (103′) according to the emphasis-driven confidencefactors, and those re-ranked results are provided to the requester(100).

Specific Examples of Operation

It is useful for understanding an invention to review several examplesof operation and usage, wherein the examples are not intended toillustrate the full scope or bounds of the various possible embodimentsof the present invention. Bearing that in mind, we refer to FIG. 3 inwhich three examples of operation an embodiment of the invention areillustrated.

Consider first a received query (101 a) of “what are the different waysto handle high cholesterol?”. The logical process as set forth inprevious paragraphs would detect the language of the NLQ as beingAmerican English from sentence structure, vocabulary, and the AmericanEnglish colloquialism “ways to handle”. It would also, from its rulesand lists, note that “cholesterol” is a species of genus “illness” orgenus “disease”, a would determine that “different” suggests the user islooking for variety. There no restriction as to source of treatmentdesired (eg. hospital, clinic, doctor, health food, dietary supplement,etc.), and there appears to be no preference for regional distinctions(e.g. Eastern medicine, Western medicine, etc.). Having built the modelfor emphasis found in the NLQ (none), the NLP search results arereceived which are, in this example, initially prioritized by mostpopular or most common treatments that anyone (folk medicine,homeopathy, MDs, eastern, etc.) may use for high cholesterol:

-   -   (1) diet,    -   (2) exercise, and    -   (3) statin drugs.

Now consider that those three “most popular” results contain one result,statin drugs, which includes emphasis on the text in its source documentor page. So, a higher confidence level would be assigned to this resultdata item, and the search results would be re-ranked (103′) and providedto the requester as:

-   -   (1) statin drugs,    -   (2) diet, and    -   (3) exercise.

Now, consider a second NLQ (101 b) of “what do doctors do about highcholesterol?”, in which the NLQ contains emphasis on the word “doctors”(bolding in this example). Because there are not regional colloquialismsin this NLQ, the vocabulary and sentence structure would be classifiedas Generalized English. And, “treatment” would be found in a synonymlist with “cure”, “remedy”, “therapy”, “regimen”, “protocol”, etc.Because bolding is known to be a way of indicated emphasis inGeneralized English script, the embodiment of the invention mayoptionally modify the NLQ to further specify preference fordoctor-prescribed or doctor-administered remedies before submitting theNLQ to the NLP search engine. And, upon receipt of the search results,the model of the results (and optionally of the revised NLQ) is used tore-rank them according to confidence factors that they are administeredor performed by a doctor as follows, which are notably different thatthose provided in the first example (101 a):

-   -   (1) niacin compounds,    -   (2) statins, and    -   (3) surgery (stints, arterial transplants).

In a third example shown in FIG. 3, an NLQ expressed in Spanish (101 c)is received, which translates to English “which doctor is best forcholesterol?”, which an underlining emphasis on “medico” (doctor). Thebase language would be detected as Spanish due to the vocabulary andsentence structure, and the underlining of “medico” is known to be amethod in Spanish script of adding emphasis. However, unlike theanalysis of the second example (101 b) NLQ which also seemed toemphasize the word doctor, the NLP analysis will detect the word “which”prior to the emphasized “medico”, and will determine that the user isinterested in all the types or varieties of doctor specializations, nottreatments, who handle cholesterol maladies. So, the NLP search results,which are initially in most popular or most frequently-appearing order,are re-prioritized by the most common or relevant practice specialtiesof doctors who treat cholesterol maladies:

-   -   (1) general practitioner (médicoamédica de medicina general),    -   (2) cardiologist (cardiólogo), and    -   (3) endocrinologist (endocrinólogo).

Please note that these top three results are radically different thanthe top three re-prioritized results of the other two examples, whichillustrates the usefulness and improved accuracy of embodiments of theinvention.

Computer Program Product

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the

Suitable Computing Platform

The preceding paragraphs have set forth example logical processesaccording to the present invention, which, when coupled with processinghardware, embody systems according to the present invention, and which,when coupled with tangible, computer readable memory devices, embodycomputer program products according to the related invention.

Regarding computers for executing the logical processes set forthherein, it will be readily recognized by those skilled in the art that avariety of computers are suitable and will become suitable as memory,processing, and communications capacities of computers and portabledevices increases. In such embodiments, the operative invention includesthe combination of the programmable computing platform and the programstogether. In other embodiments, some or all of the logical processes maybe committed to dedicated or specialized electronic circuitry, such asApplication Specific Integrated Circuits or programmable logic devices.

The present invention may be realized for many different processors usedin many different computing platforms. FIG. 4 illustrates a generalizedcomputing platform (400), such as common and well-known computingplatforms such as “Personal Computers”, web servers such as an IBMiSeries™ server, and portable devices such as personal digitalassistants and smart phones, running a popular operating systems (402)such as Microsoft™ Windows™ or IBM™ AIX™, UNIX, LINUX, Google Android™,Apple iOS™, and others, may be employed to execute one or moreapplication programs to accomplish the computerized methods describedherein. Whereas these computing platforms and operating systems are wellknown an openly described in any number of textbooks, websites, andpublic “open” specifications and recommendations, diagrams and furtherdetails of these computing systems in general (without the customizedlogical processes of the present invention) are readily available tothose ordinarily skilled in the art.

Many such computing platforms, but not all, allow for the addition of orinstallation of application programs (401) which provide specificlogical functionality and which allow the computing platform to bespecialized in certain manners to perform certain jobs, thus renderingthe computing platform into a specialized machine. In some “closed”architectures, this functionality is provided by the manufacturer andmay not be modifiable by the end-user.

The “hardware” portion of a computing platform typically includes one ormore processors (404) accompanied by, sometimes, specializedco-processors or accelerators, such as graphics accelerators, and bysuitable computer readable memory devices (RAM, ROM, disk drives,removable memory cards, etc.). Depending on the computing platform, oneor more network interfaces (405) may be provided, as well as specialtyinterfaces for specific applications. If the computing platform isintended to interact with human users, it is provided with one or moreuser interface devices (407), such as display(s), keyboards, pointingdevices, speakers, etc. And, each computing platform requires one ormore power supplies (battery, AC mains, solar, etc.).

Conclusion

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, steps, operations, elements, components, and/or groupsthereof, unless specifically stated otherwise.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

It should also be recognized by those skilled in the art that certainembodiments utilizing a microprocessor executing a logical process mayalso be realized through customized electronic circuitry performing thesame logical process(es).

It will be readily recognized by those skilled in the art that theforegoing example embodiments do not define the extent or scope of thepresent invention, but instead are provided as illustrations of how tomake and use at least one embodiment of the invention. The followingclaims define the extent and scope of at least one invention disclosedherein.

What is claimed is:
 1. A computer-implemented method comprising:retrieving, according to a natural language in which a plurality ofelectronic search results are expressed, by one or more processors, anencoding rule for a non-emphasized default text typestyle and anemphasized text typestyle, wherein the electronic search results areprimarily encoded in the default text typestyle; finding, by the one ormore processors, using the encoding rule, one or more emphasized wordsin the plurality of electronic search results relevant to a query;ranking, by the one or more processors, the plurality of electronicsearch results according to a prevalence of the found emphasized word;and producing, by the one or more processors, to user interface device,the ranked plurality of electronic search results.
 2. Thecomputer-implemented method as set forth in claim 1 wherein theplurality of electronic search results are intercepted, by one or moreprocessors, from a search engine prior to deliver to the user interfacedevice.
 3. The method as set forth in claim 1 wherein the plurality ofsearch results further comprise annotations which reflect the emphasizedcontent.
 4. The method as set forth in claim 1 further comprising, byone or more processors: revising a natural language query associatedwith the plurality of search results to expound on the emphasized querycontent; and submitting the revised query to a deep question and answersearch engine to perform a search of the corpus using the expoundednatural language query.
 5. The method as set forth in claim 1 furthercomprising, by one or more processors: receiving an indicator from theuser interface device signifying user satisfaction with one or more ofthe produced results; and employing the indicator in a subsequent searchto improve search accuracy relative to preferred and non-preferred pastresults.
 6. A computer program product comprising: a tangible,computer-readable storage memory device excluding a propagating signal;and one or more program instructions embodied by the memory device forcausing a processor to perform operations comprising: retrieving,according to a natural language in which a plurality of electronicsearch results are expressed an encoding rule for a non-emphasizeddefault text typestyle and an emphasized text typestyle, wherein theelectronic search results are primarily encoded in the default texttypestyle; finding using the encoding rule, one or more emphasized wordsin the plurality of electronic search results relevant to a query;ranking the plurality of electronic search results according to aprevalence of the found emphasized word; and producing to user interfacedevice, the ranked plurality of electronic search results.
 7. Thecomputer program product as set forth in claim 6 wherein the pluralityof electronic search results are intercepted from a search engine priorto deliver to the user interface device.
 8. The computer program productas set forth in claim 6 wherein the plurality of search results furthercomprise annotations which reflect the emphasized content.
 9. Thecomputer program product as set forth in claim 6 further comprisinginstructions embodied by the memory device for causing a processor toperform operations comprising: revising a natural language queryassociated with the plurality of search results to expound on theemphasized query content; and submitting the revised query to a deepquestion and answer search engine to perform a search of the corpususing the expounded natural language query.
 10. The computer programproduct as set forth in claim 6 further comprising instructions embodiedby the memory device for causing a processor to perform operationscomprising: receiving an indicator from the user interface devicesignifying user satisfaction with one or more of the produced results;and employing the indicator in a subsequent search to improve searchaccuracy relative to preferred and non-preferred past results.
 13. Asystem comprising: a computer processor; a tangible, computer-readablestorage memory device excluding a propagating signal; and one or moreprogram instructions embodied by the memory device for causing thecomputer processor to perform operations comprising: retrieving,according to a natural language in which a plurality of electronicsearch results are expressed an encoding rule for a non-emphasizeddefault text typestyle and an emphasized text typestyle, wherein theelectronic search results are primarily encoded in the default texttypestyle; finding using the encoding rule, one or more emphasized wordsin the plurality of electronic search results relevant to a query;ranking the plurality of electronic search results according to aprevalence of the found emphasized word; and producing to user interfacedevice, the ranked plurality of electronic search results.
 14. Thesystem as set forth in claim 13 wherein the plurality of electronicsearch results are intercepted from a search engine prior to deliver tothe user interface device.
 15. The system as set forth in claim 13wherein the plurality of search results further comprise annotationswhich reflect the emphasized content.
 16. The system as set forth inclaim 13 further comprising instructions embodied by the memory devicefor causing a processor to perform operations comprising: revising anatural language query associated with the plurality of search resultsto expound on the emphasized query content; and submitting the revisedquery to a deep question and answer search engine to perform a search ofthe corpus using the expounded natural language query.
 17. The system asset forth in claim 13 further comprising instructions embodied by thememory device for causing a processor to perform operations comprising:receiving an indicator from the user interface device signifying usersatisfaction with one or more of the produced results; and employing theindicator in a subsequent search to improve search accuracy relative topreferred and non-preferred past results.